Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations782
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.7 KiB
Average record size in memory112.2 B

Variable types

Numeric10
DateTime1
Categorical3

Alerts

cdi is highly overall correlated with sigHigh correlation
dmin is highly overall correlated with nstHigh correlation
gap is highly overall correlated with netHigh correlation
magType is highly overall correlated with net and 1 other fieldsHigh correlation
magnitude is highly overall correlated with sigHigh correlation
net is highly overall correlated with gap and 1 other fieldsHigh correlation
nst is highly overall correlated with dmin and 1 other fieldsHigh correlation
sig is highly overall correlated with cdi and 1 other fieldsHigh correlation
tsunami is highly overall correlated with magType and 1 other fieldsHigh correlation
net is highly imbalanced (88.7%) Imbalance
magType is highly imbalanced (52.7%) Imbalance
cdi has 212 (27.1%) zeros Zeros
nst has 365 (46.7%) zeros Zeros
dmin has 405 (51.8%) zeros Zeros
gap has 70 (9.0%) zeros Zeros

Reproduction

Analysis started2025-09-18 08:59:14.412890
Analysis finished2025-09-18 08:59:22.035618
Duration7.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

magnitude
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9411253
Minimum6.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:22.096740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile6.5
Q16.6
median6.8
Q37.1
95-th percentile7.8
Maximum9.1
Range2.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.445514
Coefficient of variation (CV)0.064184694
Kurtosis2.2263913
Mean6.9411253
Median Absolute Deviation (MAD)0.2
Skewness1.44444
Sum5427.96
Variance0.19848273
MonotonicityNot monotonic
2025-09-18T10:59:22.160737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6.5 131
16.8%
6.6 115
14.7%
6.7 98
12.5%
6.8 78
10.0%
6.9 77
9.8%
7 49
 
6.3%
7.1 43
 
5.5%
7.3 31
 
4.0%
7.2 30
 
3.8%
7.6 22
 
2.8%
Other values (14) 108
13.8%
ValueCountFrequency (%)
6.5 131
16.8%
6.6 115
14.7%
6.7 98
12.5%
6.8 78
10.0%
6.9 77
9.8%
7 49
 
6.3%
7.1 43
 
5.5%
7.2 30
 
3.8%
7.3 31
 
4.0%
7.4 18
 
2.3%
ValueCountFrequency (%)
9.1 2
 
0.3%
8.8 1
 
0.1%
8.6 2
 
0.3%
8.4 2
 
0.3%
8.3 3
 
0.4%
8.2 6
0.8%
8.16 1
 
0.1%
8.1 6
0.8%
8 5
0.6%
7.9 9
1.2%
Distinct773
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Minimum2001-01-01 06:57:00
Maximum2022-12-11 07:09:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-18T10:59:22.234605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:22.323803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cdi
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3337596
Minimum0
Maximum9
Zeros212
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:22.393216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.169939
Coefficient of variation (CV)0.73145243
Kurtosis-1.3577532
Mean4.3337596
Median Absolute Deviation (MAD)3
Skewness-0.19731027
Sum3389
Variance10.048513
MonotonicityNot monotonic
2025-09-18T10:59:22.442826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 212
27.1%
5 107
13.7%
7 97
12.4%
8 86
11.0%
6 77
 
9.8%
9 66
 
8.4%
4 62
 
7.9%
3 47
 
6.0%
1 14
 
1.8%
2 14
 
1.8%
ValueCountFrequency (%)
0 212
27.1%
1 14
 
1.8%
2 14
 
1.8%
3 47
 
6.0%
4 62
 
7.9%
5 107
13.7%
6 77
 
9.8%
7 97
12.4%
8 86
11.0%
9 66
 
8.4%
ValueCountFrequency (%)
9 66
 
8.4%
8 86
11.0%
7 97
12.4%
6 77
 
9.8%
5 107
13.7%
4 62
 
7.9%
3 47
 
6.0%
2 14
 
1.8%
1 14
 
1.8%
0 212
27.1%

mmi
Real number (ℝ)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9641944
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:22.488318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.462724
Coefficient of variation (CV)0.24525089
Kurtosis-0.2245919
Mean5.9641944
Median Absolute Deviation (MAD)1
Skewness-0.25040262
Sum4664
Variance2.1395614
MonotonicityNot monotonic
2025-09-18T10:59:22.542484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 209
26.7%
6 203
26.0%
5 142
18.2%
4 87
11.1%
8 68
 
8.7%
3 40
 
5.1%
9 28
 
3.6%
2 4
 
0.5%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 4
 
0.5%
3 40
 
5.1%
4 87
11.1%
5 142
18.2%
6 203
26.0%
7 209
26.7%
8 68
 
8.7%
9 28
 
3.6%
ValueCountFrequency (%)
9 28
 
3.6%
8 68
 
8.7%
7 209
26.7%
6 203
26.0%
5 142
18.2%
4 87
11.1%
3 40
 
5.1%
2 4
 
0.5%
1 1
 
0.1%

tsunami
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
478 
1
304 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters782
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

Length

2025-09-18T10:59:22.599464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-18T10:59:22.653181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

Most occurring characters

ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 478
61.1%
1 304
38.9%

sig
Real number (ℝ)

High correlation 

Distinct339
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean870.1087
Minimum650
Maximum2910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:22.711210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum650
5-th percentile650
Q1691
median754
Q3909.75
95-th percentile1550.7
Maximum2910
Range2260
Interquartile range (IQR)218.75

Descriptive statistics

Standard deviation322.46537
Coefficient of variation (CV)0.37060354
Kurtosis12.000754
Mean870.1087
Median Absolute Deviation (MAD)84
Skewness3.0836291
Sum680425
Variance103983.91
MonotonicityNot monotonic
2025-09-18T10:59:22.795384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
650 50
 
6.4%
670 41
 
5.2%
691 36
 
4.6%
711 25
 
3.2%
776 18
 
2.3%
732 15
 
1.9%
820 12
 
1.5%
651 11
 
1.4%
842 9
 
1.2%
754 9
 
1.2%
Other values (329) 556
71.1%
ValueCountFrequency (%)
650 50
6.4%
651 11
 
1.4%
652 6
 
0.8%
653 5
 
0.6%
654 3
 
0.4%
655 3
 
0.4%
656 2
 
0.3%
657 4
 
0.5%
659 1
 
0.1%
661 1
 
0.1%
ValueCountFrequency (%)
2910 2
0.3%
2840 1
0.1%
2820 1
0.1%
2790 1
0.1%
2504 1
0.1%
2397 1
0.1%
2331 1
0.1%
2184 1
0.1%
2083 1
0.1%
2074 1
0.1%

net
Categorical

High correlation  Imbalance 

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
us
747 
ak
 
11
official
 
8
nc
 
3
duputel
 
3
Other values (6)
 
10

Length

Max length8
Median length2
Mean length2.0805627
Min length2

Characters and Unicode

Total characters1,627
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowus
2nd rowus
3rd rowus
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
us 747
95.5%
ak 11
 
1.4%
official 8
 
1.0%
nc 3
 
0.4%
duputel 3
 
0.4%
at 2
 
0.3%
pt 2
 
0.3%
ci 2
 
0.3%
hv 2
 
0.3%
nn 1
 
0.1%

Length

2025-09-18T10:59:22.868516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 747
95.5%
ak 11
 
1.4%
official 8
 
1.0%
nc 3
 
0.4%
duputel 3
 
0.4%
at 2
 
0.3%
pt 2
 
0.3%
ci 2
 
0.3%
hv 2
 
0.3%
nn 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u 754
46.3%
s 747
45.9%
a 21
 
1.3%
i 18
 
1.1%
f 16
 
1.0%
c 13
 
0.8%
l 11
 
0.7%
k 11
 
0.7%
o 8
 
0.5%
t 7
 
0.4%
Other values (7) 21
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 754
46.3%
s 747
45.9%
a 21
 
1.3%
i 18
 
1.1%
f 16
 
1.0%
c 13
 
0.8%
l 11
 
0.7%
k 11
 
0.7%
o 8
 
0.5%
t 7
 
0.4%
Other values (7) 21
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 754
46.3%
s 747
45.9%
a 21
 
1.3%
i 18
 
1.1%
f 16
 
1.0%
c 13
 
0.8%
l 11
 
0.7%
k 11
 
0.7%
o 8
 
0.5%
t 7
 
0.4%
Other values (7) 21
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 754
46.3%
s 747
45.9%
a 21
 
1.3%
i 18
 
1.1%
f 16
 
1.0%
c 13
 
0.8%
l 11
 
0.7%
k 11
 
0.7%
o 8
 
0.5%
t 7
 
0.4%
Other values (7) 21
 
1.3%

nst
Real number (ℝ)

High correlation  Zeros 

Distinct312
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.25064
Minimum0
Maximum934
Zeros365
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:22.947341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median140
Q3445
95-th percentile663.95
Maximum934
Range934
Interquartile range (IQR)445

Descriptive statistics

Standard deviation250.18818
Coefficient of variation (CV)1.0865906
Kurtosis-1.0927934
Mean230.25064
Median Absolute Deviation (MAD)140
Skewness0.53330716
Sum180056
Variance62594.124
MonotonicityNot monotonic
2025-09-18T10:59:23.045318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 365
46.7%
518 4
 
0.5%
398 4
 
0.5%
282 4
 
0.5%
397 3
 
0.4%
426 3
 
0.4%
312 3
 
0.4%
352 3
 
0.4%
385 3
 
0.4%
409 3
 
0.4%
Other values (302) 387
49.5%
ValueCountFrequency (%)
0 365
46.7%
10 1
 
0.1%
20 1
 
0.1%
23 1
 
0.1%
27 1
 
0.1%
43 1
 
0.1%
50 1
 
0.1%
51 1
 
0.1%
63 1
 
0.1%
64 1
 
0.1%
ValueCountFrequency (%)
934 1
0.1%
929 1
0.1%
918 1
0.1%
862 1
0.1%
807 1
0.1%
798 1
0.1%
782 2
0.3%
774 1
0.1%
770 1
0.1%
769 1
0.1%

dmin
Real number (ℝ)

High correlation  Zeros 

Distinct369
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3257571
Minimum0
Maximum17.654
Zeros405
Zeros (%)51.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:23.144774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.863
95-th percentile5.7895
Maximum17.654
Range17.654
Interquartile range (IQR)1.863

Descriptive statistics

Standard deviation2.218805
Coefficient of variation (CV)1.6736135
Kurtosis9.283367
Mean1.3257571
Median Absolute Deviation (MAD)0
Skewness2.6045797
Sum1036.742
Variance4.9230954
MonotonicityNot monotonic
2025-09-18T10:59:23.403857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 405
51.8%
0.778 2
 
0.3%
0.289 2
 
0.3%
2.705 2
 
0.3%
1.505 2
 
0.3%
1.778 2
 
0.3%
1.487 2
 
0.3%
2.045 2
 
0.3%
3.144 2
 
0.3%
0.828 2
 
0.3%
Other values (359) 359
45.9%
ValueCountFrequency (%)
0 405
51.8%
0.04616 1
 
0.1%
0.04685 1
 
0.1%
0.07 1
 
0.1%
0.11 1
 
0.1%
0.133 1
 
0.1%
0.135 1
 
0.1%
0.142 1
 
0.1%
0.151 1
 
0.1%
0.173 1
 
0.1%
ValueCountFrequency (%)
17.654 1
0.1%
15.394 1
0.1%
12.896 1
0.1%
11.764 1
0.1%
11.411 1
0.1%
11.255 1
0.1%
10.669 1
0.1%
10.405 1
0.1%
9.799 1
0.1%
8.865 1
0.1%

gap
Real number (ℝ)

High correlation  Zeros 

Distinct256
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.03899
Minimum0
Maximum239
Zeros70
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:23.507978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.625
median20
Q330
95-th percentile55.9895
Maximum239
Range239
Interquartile range (IQR)15.375

Descriptive statistics

Standard deviation24.225067
Coefficient of variation (CV)0.96749378
Kurtosis32.027722
Mean25.03899
Median Absolute Deviation (MAD)7
Skewness4.6686068
Sum19580.49
Variance586.85386
MonotonicityNot monotonic
2025-09-18T10:59:23.620652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
9.0%
18 23
 
2.9%
22 22
 
2.8%
16 22
 
2.8%
12 20
 
2.6%
19 20
 
2.6%
17 19
 
2.4%
15 18
 
2.3%
14 18
 
2.3%
11 16
 
2.0%
Other values (246) 534
68.3%
ValueCountFrequency (%)
0 70
9.0%
8 1
 
0.1%
8.7 1
 
0.1%
9 7
 
0.9%
9.5 2
 
0.3%
10 8
 
1.0%
10.1 3
 
0.4%
10.2 1
 
0.1%
10.6 1
 
0.1%
10.8 1
 
0.1%
ValueCountFrequency (%)
239 1
0.1%
229 1
0.1%
220 1
0.1%
210 1
0.1%
208.8 1
0.1%
205.2 1
0.1%
126 1
0.1%
124 1
0.1%
123 1
0.1%
119 1
0.1%

magType
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
mww
468 
mwc
217 
mwb
70 
mw
 
16
Mi
 
4
Other values (4)
 
7

Length

Max length3
Median length3
Mean length2.9654731
Min length2

Characters and Unicode

Total characters2,319
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowmww
2nd rowmww
3rd rowmww
4th rowmww
5th rowmww

Common Values

ValueCountFrequency (%)
mww 468
59.8%
mwc 217
27.7%
mwb 70
 
9.0%
mw 16
 
2.0%
Mi 4
 
0.5%
ms 2
 
0.3%
mb 2
 
0.3%
md 2
 
0.3%
ml 1
 
0.1%

Length

2025-09-18T10:59:23.722090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-18T10:59:23.804327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mww 468
59.8%
mwc 217
27.7%
mwb 70
 
9.0%
mw 16
 
2.0%
mi 4
 
0.5%
ms 2
 
0.3%
mb 2
 
0.3%
md 2
 
0.3%
ml 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
w 1239
53.4%
m 778
33.5%
c 217
 
9.4%
b 72
 
3.1%
M 4
 
0.2%
i 4
 
0.2%
s 2
 
0.1%
d 2
 
0.1%
l 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 1239
53.4%
m 778
33.5%
c 217
 
9.4%
b 72
 
3.1%
M 4
 
0.2%
i 4
 
0.2%
s 2
 
0.1%
d 2
 
0.1%
l 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 1239
53.4%
m 778
33.5%
c 217
 
9.4%
b 72
 
3.1%
M 4
 
0.2%
i 4
 
0.2%
s 2
 
0.1%
d 2
 
0.1%
l 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 1239
53.4%
m 778
33.5%
c 217
 
9.4%
b 72
 
3.1%
M 4
 
0.2%
i 4
 
0.2%
s 2
 
0.1%
d 2
 
0.1%
l 1
 
< 0.1%

depth
Real number (ℝ)

Distinct303
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.883199
Minimum2.7
Maximum670.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-09-18T10:59:23.921358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile10
Q114
median26.295
Q349.75
95-th percentile526.9
Maximum670.81
Range668.11
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation137.27708
Coefficient of variation (CV)1.8090576
Kurtosis8.3844796
Mean75.883199
Median Absolute Deviation (MAD)14.295
Skewness3.0248691
Sum59340.662
Variance18844.996
MonotonicityNot monotonic
2025-09-18T10:59:24.026866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 92
 
11.8%
35 25
 
3.2%
20 25
 
3.2%
33 23
 
2.9%
12 21
 
2.7%
24 19
 
2.4%
11 17
 
2.2%
21 15
 
1.9%
22 13
 
1.7%
25 13
 
1.7%
Other values (293) 519
66.4%
ValueCountFrequency (%)
2.7 1
 
0.1%
4.2 2
 
0.3%
5 3
0.4%
5.81 1
 
0.1%
6 1
 
0.1%
6.43 2
 
0.3%
6.8 1
 
0.1%
7 2
 
0.3%
7.8 1
 
0.1%
8 7
0.9%
ValueCountFrequency (%)
670.81 1
0.1%
664 1
0.1%
660 1
0.1%
630.379 1
0.1%
630 1
0.1%
627.17 1
0.1%
624.464 1
0.1%
624 2
0.3%
622.73 1
0.1%
620.56 1
0.1%

latitude
Real number (ℝ)

Distinct778
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5380999
Minimum-61.8484
Maximum71.6312
Zeros0
Zeros (%)0.0%
Negative424
Negative (%)54.2%
Memory size6.2 KiB
2025-09-18T10:59:24.137182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-61.8484
5-th percentile-37.58413
Q1-14.5956
median-2.5725
Q324.6545
95-th percentile52.390155
Maximum71.6312
Range133.4796
Interquartile range (IQR)39.2501

Descriptive statistics

Standard deviation27.303429
Coefficient of variation (CV)7.7169753
Kurtosis-0.47674034
Mean3.5380999
Median Absolute Deviation (MAD)17.01635
Skewness0.20085297
Sum2766.7941
Variance745.47724
MonotonicityNot monotonic
2025-09-18T10:59:24.248288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.271 2
 
0.3%
38.276 2
 
0.3%
52.502 2
 
0.3%
52.48 2
 
0.3%
-13.571 1
 
0.1%
-36.122 1
 
0.1%
25.93 1
 
0.1%
18.443 1
 
0.1%
40.652 1
 
0.1%
-9.019 1
 
0.1%
Other values (768) 768
98.2%
ValueCountFrequency (%)
-61.8484 1
0.1%
-60.857 1
0.1%
-60.532 1
0.1%
-60.3026 1
0.1%
-60.2738 1
0.1%
-60.2627 1
0.1%
-60.2152 1
0.1%
-60.1023 1
0.1%
-58.6262 1
0.1%
-58.5446 1
0.1%
ValueCountFrequency (%)
71.6312 1
0.1%
67.631 1
0.1%
63.5144 1
0.1%
63.5141 1
0.1%
61.3464 1
0.1%
60.949 1
0.1%
60.772 1
0.1%
60.491 1
0.1%
59.6204 1
0.1%
58.775 1
0.1%

longitude
Real number (ℝ)

Distinct777
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.609199
Minimum-179.968
Maximum179.662
Zeros0
Zeros (%)0.0%
Negative261
Negative (%)33.4%
Memory size6.2 KiB
2025-09-18T10:59:24.347435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-179.968
5-th percentile-174.70275
Q1-71.66805
median109.426
Q3148.941
95-th percentile168.8589
Maximum179.662
Range359.63
Interquartile range (IQR)220.60905

Descriptive statistics

Standard deviation117.89889
Coefficient of variation (CV)2.2410317
Kurtosis-1.0883829
Mean52.609199
Median Absolute Deviation (MAD)52.729
Skewness-0.70298243
Sum41140.394
Variance13900.147
MonotonicityNot monotonic
2025-09-18T10:59:24.451152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168.892 2
 
0.3%
-168.08 2
 
0.3%
168.143 2
 
0.3%
-167.736 2
 
0.3%
107.419 2
 
0.3%
159.596 1
 
0.1%
178.331 1
 
0.1%
128.425 1
 
0.1%
-72.571 1
 
0.1%
-124.692 1
 
0.1%
Other values (767) 767
98.1%
ValueCountFrequency (%)
-179.968 1
0.1%
-179.511 1
0.1%
-179.373 1
0.1%
-178.959 1
0.1%
-178.927 1
0.1%
-178.804 1
0.1%
-178.6 1
0.1%
-178.57 1
0.1%
-178.4 1
0.1%
-178.346 1
0.1%
ValueCountFrequency (%)
179.662 1
0.1%
179.544 1
0.1%
179.35 1
0.1%
179.146 1
0.1%
178.735 1
0.1%
178.381 1
0.1%
178.363 1
0.1%
178.331 1
0.1%
178.291 1
0.1%
178.278 1
0.1%

Interactions

2025-09-18T10:59:21.200257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:14.879248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.595226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.170385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.825152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.421894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.336185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.031110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.675519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.363072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.268821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.046767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.649307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.233069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.883087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.518446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.411275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.093299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.741343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.435915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.338627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.101480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.703057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.292593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.937499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.587134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.477475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.155027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.808495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.495259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.412373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.166698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.766357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.368505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.001007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.649691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.553127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.222349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.877773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.564082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.480964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.225672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.824029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.437889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.057074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.884617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.621136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.283937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.943173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.626514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.543167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.288670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.878062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.499878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.115821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.948794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.685573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.353159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.009765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.689853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.612126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.348993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.941011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.565608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.178724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.034177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.753320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.423294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.079689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.764140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.674464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.409555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.995699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.627597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.236238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.108552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.820238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.483117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.146118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.826203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.740999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.469774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.055789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.695178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.300941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.200068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.889381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.546319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.218180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.021243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.804109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:15.532311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.111225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:16.757962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:17.359836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.269418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:18.960218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:19.611272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:20.294820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-18T10:59:21.089168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-18T10:59:24.541218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cdidepthdmingaplatitudelongitudemagTypemagnitudemminetnstsigtsunami
cdi1.000-0.0400.1750.1250.138-0.1960.1370.2340.3530.019-0.2000.5730.277
depth-0.0401.0000.049-0.185-0.0760.0270.0000.124-0.2540.0000.0080.0390.054
dmin0.1750.0491.0000.072-0.193-0.0780.133-0.091-0.2590.000-0.813-0.0630.404
gap0.125-0.1850.0721.000-0.015-0.2840.344-0.137-0.0040.514-0.041-0.0080.047
latitude0.138-0.076-0.193-0.0151.000-0.1090.173-0.0150.1520.1950.1590.1720.323
longitude-0.1960.027-0.078-0.284-0.1091.0000.1760.024-0.0890.2130.103-0.1790.419
magType0.1370.0000.1330.3440.1730.1761.0000.0120.0990.6540.2750.1350.610
magnitude0.2340.124-0.091-0.137-0.0150.0240.0121.0000.2590.3080.1030.7690.033
mmi0.353-0.254-0.259-0.0040.152-0.0890.0990.2591.0000.1200.1450.4360.163
net0.0190.0000.0000.5140.1950.2130.6540.3080.1201.0000.0000.2030.095
nst-0.2000.008-0.813-0.0410.1590.1030.2750.1030.1450.0001.0000.0380.639
sig0.5730.039-0.063-0.0080.172-0.1790.1350.7690.4360.2030.0381.0000.081
tsunami0.2770.0540.4040.0470.3230.4190.6100.0330.1630.0950.6390.0811.000

Missing values

2025-09-18T10:59:21.905913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-18T10:59:21.980827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

magnitudedate_timecdimmitsunamisignetnstdmingapmagTypedepthlatitudelongitude
07.022-11-2022 02:03871768us1170.50917.0mww14.000-9.7963159.5960
16.918-11-2022 13:37440735us992.22934.0mww25.000-4.9559100.7380
27.012-11-2022 07:09331755us1473.12518.0mww579.000-20.0508-178.3460
37.311-11-2022 10:48551833us1491.86521.0mww37.000-19.2918-172.1290
46.609-11-2022 10:14021670us1314.99827.0mww624.464-25.5948178.2780
57.009-11-2022 09:51431755us1424.57826.0mwb660.000-26.0442178.3810
66.809-11-2022 09:38131711us1364.67822.0mww630.379-25.9678178.3630
76.720-10-2022 11:57761797us1451.15137.0mww20.0007.6712-82.3396
86.822-09-2022 06:168711179us1752.13792.0mww20.00018.3300-102.9130
97.619-09-2022 18:059811799us2711.15369.0mww26.94318.3667-103.2520
magnitudedate_timecdimmitsunamisignetnstdmingapmagTypedepthlatitudelongitude
7727.124-02-2001 07:23070776us4260.00.0mwc35.01.2710126.249
7737.413-02-2001 19:28060842us2210.00.0mwc36.0-4.6800102.562
7746.613-02-2001 14:22080670us2730.00.0mwc10.013.6710-88.938
7757.726-01-2001 03:16090912us4720.00.0mwc16.023.419070.232
7766.916-01-2001 13:25060732us1170.00.0mwb28.0-4.0220101.776
7777.713-01-2001 17:33080912us4270.00.0mwc60.013.0490-88.660
7786.910-01-2001 16:02570745ak00.00.0mw36.456.7744-153.281
7797.109-01-2001 16:49070776us3720.00.0mwb103.0-14.9280167.170
7806.801-01-2001 08:54050711us640.00.0mwc33.06.6310126.899
7817.501-01-2001 06:57070865us3240.00.0mwc33.06.8980126.579